• Journal of Environmental Science International
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• v.19 no.6
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• pp.689-701
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• 2010

The effects of high-resolution wind profiler (HWP) data on the wind distributions were evaluated in two different coastal areas during the study period (23-26 August, 2007), indicating weak-gradient flows. The analysis was performed using the Weather Research and Forecasting (WRF) model coupled with a three-dimensional variational (3DVAR) data assimilation system. For the comparison purpose, two coastal regions were selected as: a southwestern coastal (SWC) region characterized by a complex shoreline and a eastern coastal (EC) region surrounding a simple coastline and high mountains. The influence of data assimilation using the HWP data on the wind distributions in the SWC region was moderately higher than that of the EC region. In comparison between the wind speed and direction in the two coastal areas, the application of the HWP data contributed to improvement of the wind direction distribution in the SWC region and the wind strength in the EC region, respectively. This study suggests that the application of the HWP data exerts a large impact on the change in wind distributions over the sea and thus can contribute to the solution to lack of satellite and buoy data with their observational uncertainty.

• Journal of Climate Change Research
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• v.6 no.3
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• pp.193-201
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• 2015

The aim of this study is to find preferred climate condition for outdoor water activity and to estimate future change of preferred season for the activity following the climate change. We chose urban public swimming pools, Hangang park swimming pools, which do not have any attractions except pools and allow people to make decision to visit pools in the morning solely based on the weather conditions as study sites. We identified the preferred climate conditions by analyzing the relationship between number of visitors and temperature, wind chill temperature and discomfort indexes. According to the result, the preferred temperature range was from $23.51^{\circ}C$ to $37.56^{\circ}C$, the wind chill temperature range was from $25.90^{\circ}C$ to $39.43^{\circ}C$, the discomfort index range was from 71.61 to 88.98 and the precipitation range was below 22.8 mm per day. When the temperature range is applied as the preferred season, in present, the length of the season is 127 days, from end of May to end of September. However, if temperature increase resulting from lower emission scenario (RCP 6.0), the season would be extended to 162 days, from early May to middle of October. If temperature is increasing under high emission scenario (RCP 8.5), the length of the season would be extended to 173 days from early May to end of October. In addition, the period of between end of July and early August, which is currently the most preferred season, would not be favored anymore due to high temperature. The result of this study further suggests the necessity of climate change adaptation activities.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.8-14
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• 2020

The human impact of particulate matter are revealed and demand for improved forecast accuracy is increasing. Recently, efforts is made to improve the accuracy of PM10 predictions by using machine learning, but prediction performance is decreasing due to the particulate matter data with a large rate of low concentration occurrence. In this paper, separation prediction model by concentration is proposed to improve the accuracy of PM10 particulate matter forecast. The low and high concentration prediction model was designed using the weather and air pollution factors in Cheonan, and the performance comparison with the prediction models was performed. As a result of experiments with RMSE, MAPE, correlation coefficient, and AQI accuracy, it was confirmed that the predictive performance was improved, and that 20.62% of the AQI high-concentration prediction performance was improved.

• Journal of Environmental Policy
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• v.14 no.1
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• pp.53-83
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• 2015

Recently, climate change risk assessment has been discussed as a medium process for making climate change adaptation policies in the research field of climate change adaptation. Climate change risk assessment has been understood to have an intermediary role among impact assessment, vulnerable assessment and policy, and is used in the process of devising adaptation policies in the United Kingdom (UK). This paper quantitatively assessed the risks of climate change in Korea, applied the methods used in the UK, underwent the classification process and suggested implications of Korean adaptation policies. A survey of experts, based on Delphi's method and the classification criterion developed by Klinke and Renn(2002), was also carried out. A list of climate risks was created from the climate change impact and vulnerability assessment report of Korea, first national adaptation policy of Korea, and general climate risks of the UK. From the results, 42 risks out of total 125 risks were selected based on their importance. The assessed risks with factors, such as high impact and urgency, are related to repeated and large scale damage from storms and floods caused by abnormal or extreme weather events. Ecological changes and social infrastructure risks were engaged as required as a policy response for medium to longer term. As for making the classification, types of climate risks were suggested to manage the basic capacity in relation to social trust, triggering mechanism and responsibility. Following suggestions are put forward as the base of autonomous adaptation: increasing the capacity of civil society, mutual trust and civil participation in adaptation policy process.

• Journal of Environmental Impact Assessment
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• v.25 no.4
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• pp.296-306
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• 2016

There is a growing interest in establishing a regional climate change adaptation policy as the climate change impact in the region and local scale increases. This study focused on the analysis of 32 regions on its characteristics of local climate change adaptation plans. First, statistic program R was used for conducting cluster analysis based on the frequency and budgets of adaptation plan. Further, we analyzed damage frequency from newspapers regarding climate change impacts in eight categories which were caused by extreme weather events on 2,565 cases for 24 years. Lastly, the characteristics of climate change adaptation plan was compared with damage frequency patterns for evaluating the adequacy of climate change adaptation plan on each cluster. Four different clusters were created by cluster analysis. Most clusters clearly have their own characteristics on certain sectors. There was a high frequency of damage in 'disaster' and 'health' sectors. Climate change adaptation plan and budget also invested a lot on those sectors. However, when comparing the relative rate among regional governments, there was a difference between types of damage and climate change adaptation plan. We assumed that the difference could come from that each region established their adaptation plans based on not only the frequency of damage, but vulnerability assessment, and expert opinions as well. The result of study could contribute to policy making of climate change adaptation plan.

• Journal of Environmental Impact Assessment
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• v.33 no.1
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• pp.9-17
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• 2024

The purpose of this study is to select and predict optimal heatwave indices for describing and predicting heat-related illnesses. Regression analysis was conducted using Heat-related illness surveillance system data for a number of heat-related illnesses and meteorological data from the Korea Meteorological Administration's Automatic Weather Station (AWS) for the period from 2021 to 2023. Daily average temperature, daily maximum temperature, daily average Wet Bulb Globe Temperature (WBGT), and daily maximum WBGT values were calculated and analyzed. The results indicated that among the four indicators, the daily maximum WBGT showed the highest suitability with an R² value of 0.81 and RMSE of 0.98, with a threshold of 29.94 Celsius. During the entire analysis period, there were a total of 91 days exceeding this threshold, resulting in 339 cases of heat-related illnesses. Predictions of heat-related illness cases from 2021 to 2023 using the regression equation for daily maximum WBGT showed an accuracy with less than 10 cases of error annually, demonstrating a high level of precision. Through continuous research and refinement of data and analysis methods, it is anticipated that this approach could contribute to predicting and mitigating the impact of heatwaves.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.35-49
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• 2014

In order to understand the impacts of surface characteristics and the distance from the urban heat island center to suburban areas on the mean night time air temperature, we analyzed GIS and AWS observational data. Spatial distributions of mean night time air temperature during the summer and winter periods of 2004-2011(six years) were utilized. Results show that the temperature gradients were different by distance and direction. We found high correlation between mean night-time air temperature and artificial land cover area within 1km and 200m radii during both summer(R=0.84) and winter(R=0.78) seasons. Regression models either from 1km and 200m land surface data explained the distribution of night-time temperature equally well if the input data had sufficient resolution with detailed attribute including building area and height.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.1
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• pp.20-25
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• 2020

Spatial simulation of crop growth often requires application of management conditions to each cell. In particular, it is of great importance to determine the temperature conditions during the nursery period for rice seedlings, which would affect heading date projections. The objective of this study was to determine the value of TMPSB, which is the parameter of ORYZA2000 model to represent temperature increase under a plastic tunnel during the rice seedling periods. Candidate values of TMPSB including 0℃, 2℃, 5℃, 7℃ and 9℃ were used to simulate rice growth and yield. Planting dates were set from mid-April to mid-June. The simulations were performed at four sites including Cheorwon, Suwon, Seosan, and Gwangju where climate conditions at rice fields common in Korea can be represented. It was found that the TMPSB values of 0℃ and 2℃ resulted in a large variation of heading date due to low temperature occurred in mid-April. When the TMPSB value was >7℃, the variation of heading date was relatively small. Still, the TMPSB value of 5℃ resulted in the least variation of heading date for all the planting dates. Our results suggested that the TMPSB value of 5℃ would help reasonable assessment of climate change impact on rice production when high resolution gridded weather data are used as inputs to ORYZA2000 model over South Korea.

• Journal of Preventive Medicine and Public Health
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• v.38 no.3
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• pp.283-290
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• 2005

Objectives : The aim of this paper was to examine the relationship between the summertime (June to August) heat index, which quantifies the bioclimatic apparent temperature in sultry weather, and the daily disease-related mortality in Seoul for the period from 1991 to 2000. Methods : The daily maximum (or minimum) summertime heat indices, which show synergetic apparent temperatures, were calculated from the six hourly temperatures and real time humidity data for Seoul from 1991 to 2000. The disease-related daily mortality was extracted with respect to types of disease, age and sex, etc. and compared with the time series of the daily heat indices. Results : The summertime mortality in 1994 exceeded the normal by 626 persons. Specifically, blood circulation-related and cancer-related mortalities increased in 1994 by 29.7% (224 persons) and 15.4% (107 persons), respectively, compared with those in 1993. Elderly persons, those above 65 years, were shown to be highly susceptible to strong heat waves, whereas the other age and sex-based groups showed no significant difference in mortality. In particular, a heat wave episode on the 22nd of July 2004 ($>45^{\circ}C$ daily heat index) resulted in double the normal number of mortalities after a lag time of 3 days. Specifically, blood circulation-related mortalities, such as cerebral infraction, were predominant causes. Overall, a critical mortality threshold was reached when the heat index exceeded approximately $37^{\circ}C$, which corresponds to human body temperature. A linear regression model based on the heat indices above $37^{\circ}C$, with a 3 day lag time, accounted for 63% of the abnormally increased mortality (${\geq}+2$ standard deviations). Conclusions : This study revealed that elderly persons, those over 65 years old, are more vulnerable to mortality due to abnormal heat waves in Seoul, Korea. When the daily maximum heat index exceeds approximately $37^{\circ}C$, blood circulation-related mortality significantly increases. A linear regression model, with respect to lag-time, showed that the heat index based on a human model is a more dependable indicator for the prediction of hot weather-related mortality than the ambient air temperature.

• Atmosphere
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• v.26 no.1
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• pp.19-33
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• 2016

Water vapor in the atmosphere is an important element that generates various meteorological phenomena and modifies a hydrological cycle. In general, the Yeongdong region has a lot of snow compared to the other regions in winter due to the complex topography and an adjacent East Sea. However, the phase change from water vapor to ice cloud and further snowfall has little been examined in detail. Therefore, in this study, we investigated phase change of liquid water in terms of a quantitative budget as well as time lag of water vapor conversion to snowfall in the ESSAY (Experiment on Snow Storms At Yeongdong) campaign that had been carried out from 2012 to 2015. First, we classified 3 distinctive synoptic patterns such as Low Crossing, Low Passing, and Stagnation. In general, the amount of water vapor of Low Crossing is highest, and Low Passing, Stagnation in order. The snowfall intensity of Stagnation is highest, whereas that of Low Crossing is the lowest, when a sharp increase in water vapor and accordingly a following increase in precipitation are shown with the remarkable time lag. Interestingly, the conversion rate of water vapor to snowfall seems to be higher (about 10%) in case of the Stagnation type in comparison with the other types at Bukgangneung, which appears to be attributable to significant cooling caused by cold surge in the lower atmosphere. Although the snowfall is generally preceded by an increase in water vapor, its amount converted into the snowfall is also controlled by the atmosphere condition such as temperature, super-saturation, etc. These results would be a fundamental resource for an improvement of snowfall forecast in the Yeongdong region and the successful experiment of weather modification in the near future.